Various imaging systems and tools have been developed to assist physicians, clinicians, radiologists, etc. in evaluating medical images to diagnose medical conditions. For example, computer-aided detection (CAD) tools have been developed for various clinical applications to provide automated detection of medical conditions captured in medical images, such as colonic polyps and other abnormal anatomical structures such as lung nodules, lesions, aneurysms, calcification, in breast, heart or artery tissue.
The morphology of a lesion, such as its shape or boundary (i.e. margin), provides useful clues in identifying whether the lesion is malignant or not. For example, FIGS. 1a-b show middles slices of lesions with various types of shapes and boundaries respectively. As illustrated in FIG. 1a, lesions may be classified according to: (A) round, (B) oval, (C) lobulated or (D) irregular shapes. Lesions may also be classified based on: (A) smooth, (B) irregular or (C) spiculated boundaries, as shown in FIG. 1b. A lesion with spikes (i.e. spiculation) or an irregular boundary or shape is more likely to be malignant than a lesion with a smooth boundary or round (or oval) shape. In other words, smoothness of a lesion shape or boundary is related to benignity while irregularity is related to malignancy.
Classifiers may be developed to distinguish lesions based on their morphologies so as to facilitate the diagnosis of cancer. Various methods have been proposed to classify two-dimensional (2D) binary images based on shape context, segment sets, mode-based approaches and shock graphs. Typically, the contours of binary images are parameterized by a normalized arc length (a) to yield a pair of univariate functions (x(a), y(a)), with only one rotational Degree of Freedom (DOF). The origin may be fixed to circumvent the problem of handling the rotational DOF. Such parameterization facilitates the use of many convenient tools such as Fourier analysis, wavelet decomposition, and Principal Component Analysis (PCA).
It is difficult, however, to employ similar strategies for three-dimensional (3D) grayscale images because of the difficulty in parameterizing 3D grayscale images consistently. The parameterization techniques used for 2D binary images do not apply to 3D grayscale images. Cartesian coordinates are useless because they depend on origins and posture, which are either unrelated to the objects in 3D images, absent or undefined.
Therefore, there is a need to provide a technology that effectively classifies objects in 3D volume sets of grayscale images with high accuracy and computational efficiency.